Because of the importance of fresh produce in the U.S. to both the economy and
people’s health, it is essential to maintain the quality of this valuable resource. In this
project, sensor fusion technology was applied to two artificial noses: the Cyranose 320
electronic nose (Enose) and a surface acoustic wave sensor (zNoseTM), in order to
develop a system for non-destructive, rapid detection the safety and quality of fresh
produce.
Dominant volatile compounds associated with healthy apples and physically
damaged apples were identified by gas chromatography and mass spectrometry (GC-
MS). The results proved that the volatile compounds from healthy apples and damaged
apples are different both qualitatively and quantitatively.
The Enose and zNoseTM were firstly independently studied. Different statistical
models, such as PLS-DA and PLS-CVA, were developed and performed on the data on
individual days and one single month. It was found that statistical models were effective
for separating healthy from damaged apples when individual days or single month data
were analyzed. When data from different months were combined, statistical models
could not give desirable results due to the non-linearity of this problem. In order to
improve the system classification performance, artificial neural networks (ANN) were
used to develop classification models. Three ANN models (back-propagation,
probabilistic, and learning vector quantification networks) were developed and tested on
data sets collected in three different months. Results showed that all three ANN models
achieved better classification performance than statistical models when data from
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different months were pooled together for both the Enose and zNoseTM data. Among
these three ANN models, the PNN was superior, considering the classification quality
(85% and 77% classification accuracy for the Enose and zNoseTM respectively) and
efficiency (training was faster than BP and LVQ).
Another focus of this research was to reduce data dimensionality of the Enose and
zNoseTM. Various methods were investigated towards this end. Although methods such
as the PCA loadings method, F-value selection and sequential forward/backward search
reduced data dimensionality to various degrees, evolutionary algorithms were proven to
be a more powerful and robust search approach. Evolutionary algorithms reduced data
dimensionality 75% and 50% for the Enose and zNoseTM respectively, and the
classification error rate for the two instruments was reduced by 10% for the Enose and
20% for the zNoseTM.
Multisensor data fusion models both at the feature and decision levels were
developed to combine the Enose and zNoseTM data to improve classification
performance. At the feature level, ANN-based fusion models (dynamic selective fusion)
reduced the classification error rate to 1.8% on average in 30 independent runs, and at the
same time only about 50% of the sensors from the Enose and zNose
TM were used for
input. At the decision level fusion, a Bayesian network was developed to combine
classification decisions made by the Enose and zNoseTM classifiers independently. It was
found that using soft evidence produced by the BP classifier either with or without prior
performance knowledge gave the best improvement of classification performance.
Finally, trained models were tested on new data sets which were collected by
measuring the presence of one bad apple placed amongst three good apples in a 4 L
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concentration chamber. Sensor fusion models could achieve 81% and 82% classification
accuracy at the feature level and decision level; when selected sensors were updated, the
classification accuracy of sensor fusion models were improved to 97% at the feature level
and 91% at the decision level.
This study introduced three artificial intelligent technologies into food quality and
safety evaluation: artificial neural networks, evolutionary algorithms, and multisensor
data fusion, utilizing information from two advanced volatile detection instruments.
Sophisticated algorithms improved the performance of two artificial noses and showed
promise of eventually achieving non-destructive detection of physically damaged and
fungi-diseased apples.
Key words: electronic nose, surface acoustic wave sensor, zNoseTM, artificial nose, gas
sensors, olfaction, odor, artificial neural networks, evolutionary algorithms, multisensor
data fusion, genetic algorithms, covariance matrix adaptation evolutionary algorithms,
differential evolution, probabilistic neural networks, back-propagation neural networks,
learning vector quantification networks, principal component analysis, partial least
square, discriminant analysis
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